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and arise in Equations 1.21 and 1.22 by making the homogeneous, macroscopic approximation that B = H and D = E . Often, we will use these equations for a good, nonmagnetic conductor in which there is relatively little polarization due to the electric dipole character of the propagating medium (e.g., copper) so we may use 0 and 0 in that application. In that case, k2 2 0 0(1 j / 0), and we can use a mathematical convenience of de ning an effective r,eff = (1 j / 0), which takes into account the conductivity as if it were part of the permittivity constant. Two warnings for later analysis: (1) we multiplied and divided by in factoring out 2 so we cannot consider r,eff(0) without remembering that the correct term to consider is lim ( ), and (2) D = 0 r,eff( )E only insofar as the permittivity contains the conductivity (i.e., r,eff( ) is not expressing the alignment of polar molecules).

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of t On one day we obtain a total of 8640 data points in a bi-dimensional space (recall that we are using 10 s time bin) In order to summarize this data, we t the 8640 data points representing a single-day trajectory with a quadratic curve, which is a degree-two polynomial In addition to the variance, the same procedure is applied also to the skewness, so as to observe the skewness-mean trajectory The resulting curves are given in the left-hand plot of Figure 103 for 22 days The dashed and continuous lines refer respectively to days 6 20 (with the bottleneck) and days 21 27 (after the bottleneck removal) The variance-mean trajectory is such that when the bottleneck is in place the variance reaches a maximum when the mean is approximately half of the bottleneck capacity, after which it decreases again.

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Instead, without the bottleneck the variance keeps increasing steadily with the mean traf c intensity The presence of the bottleneck also impacts the skewness of the distribution: in the high traf c region the bottleneck clearly induces left-skewness ( < 0), while after the bottleneck removal the distributions tend to be more symmetric, with small positive values of We applied a similar method to the percentiles p10 (t) and p90 (t) The resulting regressed trajectories are shown in the right-hand plot of Figure 103, from which it can be easily seen that the trajectory of the inter-percentile distance {p90 (t) p10 (t), m(t)} displays a concave behaviour similar to the variance Interpretation of the Results All the above observations are consistent with the following interpretation The empirical rate distribution evolves with the mean traf c intensity therefore with the time-of-day as outlined in Figure 104.

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For low traf c intensity (night and early morning) the distribution is right-skewed: since rates cannot be negative only positive uctuations are possible For higher traf c intensity (morning and afternoon) the rate distribution becomes more dispersed (higher variance) and more symmetric (lower skewness) In fact, the rate of individual connections is limited by the bandwidth of the radio link, but such a limit caps individual ows (per-MS channel) or at most on a bundle of a few ows belonging to the active MSs in the same.

to define the kind of testing that measures the speed of software to analyze techniques that simplify the intrinsically complex performance testing of

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